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What are the areas of text mining in data mining?
Text mining is also known as text analysis. It is the procedure of transforming unstructured text into structured data for simple analysis. Text mining applies natural language processing (NLP), enabling machines to know the human language and process it automatically.
It is defined as the procedure of extracting significant information from standard language text. Some data that it can generate via text messages, records, emails, files are written in common language text. Text mining is generally used to draw beneficial insights or patterns from such data.
There are the following areas of text mining in data mining which are as follows −
Information Retrieval − Information retrieval is considered as an addition to file retrieval and the texts that are declared are processed to consolidate. Therefore document retrieval is followed by a text summarization procedure and targets on the query formal by the user.
IR systems support narrowing down the set of records that are relevant to a specific problem. Text mining involves using very complex algorithms to generous document collections. Also, IR can advance up the analysis significantly by decreasing the number of documents.
Data Mining − Data mining is the process of finding useful new correlations, patterns, and trends by transferring through a high amount of data saved in repositories, using pattern recognition technologies including statistical and mathematical techniques. It is the analysis of factual datasets to discover unsuspected relationships and to summarize the records in novel methods that are both logical and helpful to the data owner.
In Data mining, hidden patterns of data are considered according to the multiple categories into a piece of useful data. This data is assembled in an area including data warehouses for analyzing it, and data mining algorithms are performed. This data facilitates in creating effective decisions which cut value and increase revenue.
Natural Language Processing (NLP) − NLP is the art of human language. The purpose of NLP in text mining is to deliver the system in the data extraction process as an input.
The development of the NLP application is hard because computers usually require humans to "Speak" to them in a programming language that is specific, free, and exceptionally structured. Human speech is regularly not authentic so that it can be based on many complex variables, including slang, social context, and regional dialects.
Information Extraction (IE) − Information Extraction is the task of automatically extracting structured data from unstructured. In general cases, this activity involves processing human language texts using NLP.
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